How to Leverage Machine Learning to Improve Your Marketing

AI and machine learning are now everyday buzzwords in this ever-evolving, technology-driven world. The marketing niche is no exception.

Machine learning and AI can be seen everywhere, from highly personalized suggestions on your shopping app to automated chatbot responses.

This article will uncover the basics of machine learning and the advantages of leveraging ML in marketing.

We will also discuss how Datameer can help you harness the power of machine learning, so stay tuned!

Data – The Bedrock of Machine Learning

What is Machine Learning?

Machine Learning is a broad term with countless definitions depending on the context.

The countless variations make sense because ML spans entire families of techniques for making inferences and predictions on data.

For this article, we will stick with a relatively simple definition coined by Yufeng Guo, a developer advocate at Google:

 “Machine learning at its core can be defined as using Data (training) to answer questions (make predictions).”

The Data Pyramid

“A Machine algorithm is only as good as the data you feed it,” hence the need for clean and top-quality data.

This diagram encapsulates the stages required to achieve quality data and, as a result, leverage the benefits of machine learning within your organization.

A typical marketing analytics reporting stack should contain tools that perform :

• Data Acquisition
• Data Engineering
• Master Data Engineering and Governance
• Reporting and Business Intelligence
• And then perhaps feed reliable, clean datasets that you produce into machine learning systems → Data Science.

We will cover these steps in detail in a future article.

Benefits and Use Cases for ML and AI in Marketing

Imagine if you could determine which lead is a good fit for your product and which is the most promising.

Or maybe optimize customer experience with conversational chatbots, saving time and possibly freeing up the marketing budget for more strategic initiatives.

All this and more are possible with the help of Machine learning technology…

There are endless benefits of leveraging machine learning, and in this section, we will take a look at some examples of companies that are doing it right.

1 – Starbucks AI

According to a Geekwire publication in 2016, Starbucks leveraged artificial intelligence for customer personalization to boost sales.

The Starbucks mobile app, drive-through screens, and digital menu boards served as data points that fed their real-time personalization engine.

This approach helped with behavioral segmentation and enabled Starbucks to recommend what their customers were most likely to order—as a result, making them feel valued.

Additionally, In 2017, Starbuck rolled out their version of Siri, “my Starbucks Barista,” and here’s what Gerri Martin-Flickinger, chief technology officer at Starbucks, had to say about that.

The Starbucks experience is built on the personal connection between our barista and customer, so everything we do in our digital ecosystem must reflect that sensibility.

2 – Frase AI

Another good example is Frase.io – Frase is an AI tool that helps marketing teams looking to create optimized SEO content with AI for functionality that yields higher ROI.

Frase leverages AI and machine learning to aid topic and keyword research, optimized content briefs, and write-ups.

Tons of SEO teams, including Neil Patel, Shopify, and Microsoft, have testimonials on how this tool has been a gamechanger in their SEO writing.

A Machine Learning Oriented Marketing Strategy

So let’s assume you already have a steady inflow of high-quality data within your marketing analytics stack, and your organization is ready to take that leap into Machine Learning.

There are four elements that an organization needs to put in place to harness the potential of AI and predictive analytics:

1. Having the correct hypothesis – Before embarking on a machine learning journey, defining your ML marketing goal upfront is essential. By setting assessable goals and defining what success is, you assist the data science team in the building phase of the ML models.
2. Quality data – Data must be formatted, clean, and organized. Refer to Data Pyramid
3. Using the right tools – There are many ML and AI tools for different use cases. Examples of good ML tools are Analytics Intelligence in Google Analytics and Tensor flow for developing ML models, etc.
4. Having the right people with the right mindset – Having a cross-functional and diverse set of experts on your ML team is a catalyst for successful outcomes.

A Tool That Helps With Creating Quality Data

Now we understand these concepts, let’s talk about how Datameer ties in with all this.

Datameer is a SaaS tool that sits between Data Engineering, data governance, and core BI Business Intelligence.

It’s a multi-persona transformation tool used to clean and create reliable data models used in your machine learning processes.

Integrate Datameer with your Snowflake environment today and kickstart your machine learning and AI adoption journey. Get started with your free Datameer trial now!